code: def plot_cache(
      results,
      dsk,
      start_time,
      end_time,
      metric_name,
      palette="Viridis",
      label_size=60,
      **kwargs,
  ):
      """Visualize the results of profiling in a bokeh plot.
  
      Parameters
      ----------
      results : sequence
          Output of CacheProfiler.results
      dsk : dict
          The dask graph being profiled.
      start_time : float
          Start time of the profile in seconds
      end_time : float
          End time of the profile in seconds
      metric_name : string
          Metric used to measure cache size
      palette : string, optional
          Name of the bokeh palette to use, must be a member of
          bokeh.palettes.all_palettes.
      label_size: int (optional)
          Maximum size of output labels in plot, defaults to 60
      **kwargs
          Other keyword arguments, passed to bokeh.figure. These will override
          all defaults set by visualize.
  
      Returns
      -------
      The completed bokeh plot object.
      """
      bp = import_required("bokeh.plotting", _BOKEH_MISSING_MSG)
      from bokeh.models import HoverTool
  
      defaults = dict(
          title="Profile Results",
          tools="hover,save,reset,wheel_zoom,xpan",
          toolbar_location="above",
          width=800,
          height=300,
      )
      # Support plot_width and plot_height for backwards compatibility
      if "plot_width" in kwargs:
          kwargs["width"] = kwargs.pop("plot_width")
          if BOKEH_VERSION().major >= 3:
              warnings.warn(
                  "Use width instead of plot_width with Bokeh >= 3"
              )
      if "plot_height" in kwargs:
          kwargs["height"] = kwargs.pop("plot_height")
          if BOKEH_VERSION().major >= 3:
              warnings.warn(
                  "Use height instead of plot_height with Bokeh >= 3"
              )
      defaults.update(**kwargs)
  
      if results:
          starts, ends = list(zip(*results))[3:]
          tics = sorted(unique(starts + ends))
          groups = groupby(
              lambda d: pprint_task(d[1], dsk, label_size), results
          )
          data = {}
          for k, vals in groups.items():
              cnts = dict.fromkeys(tics, 0)
              for v in vals:
                  cnts[v.cache_time] += v.metric
                  cnts[v.free_time] -= v.metric
              data[k] = [0] + list(
                  accumulate(add, pluck(1, sorted(cnts.items())))
              )
  
          tics = [0] + [i - start_time for i in tics]
          p = bp.figure(x_range=[0, end_time - start_time], **defaults)
  
          for (key, val), color in zip(
              data.items(), get_colors(palette, data.keys())
          ):
              p.line(
                  "x",
                  "y",
                  line_color=color,
                  line_width=3,
                  source=bp.ColumnDataSource(
                      {"x": tics, "y": val, "label": [key for i in val]}
                  ),
              )
  
      else:
          p = bp.figure(y_range=[0, 10], x_range=[0, 10], **defaults)
      p.yaxis.axis_label = f"Cache Size ({metric_name})"
      p.xaxis.axis_label = "Time (s)"
  
      hover = p.select(HoverTool)
      hover.tooltips = """
      <div>
          <span style="font-size: 14px; font-weight: bold;">Task:</span>&nbsp;
          <span style="font-size: 10px; font-family: Monaco, monospace;">@label</span>
      </div>
      """
      return p
  
file_path: dask/dask/diagnostics/profile_visualize.py
imports: from __future__ import annotations
  import random
  import warnings
  from bisect import bisect_left
  from itertools import cycle
  from operator import add, itemgetter
  from tlz import accumulate, groupby, pluck, unique
  from dask.core import istask
  from dask.utils import apply, funcname, import_required
label_desc: Use image node_id 9 for calling a global function with example usage: plot_cache(results, dsk, start_time, end_time, metric_name, palette, label_size) and returns: p